Comparing all your movies with TF-IDF

Now that you have put in the hard work of getting your TF-IDF data into a usable format, it's time to put it to work generating finding similarities and generating recommendations.

This time as you are using TF-IDF scores (which are floats as opposed to Booleans) you will use the cosine similarity metric to find the similarities between items. In this exercise, you will generate a matrix of all of the movie cosine similarities and store them in a DataFrame for ease of lookup. This will allow you to compare movies and find recommendations quickly and easily.

The tfidf_df DataFrame you created in the last exercise containing a row for each movie has been loaded for you.

Cet exercice fait partie du cours

Building Recommendation Engines in Python

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Instructions

Find the cosine similarity measures between all movies and assign the results to cosine_similarity_array.
Create a DataFrame from the cosine_similarity_array with tfidf_summary_df.index as its rows and columns.
Print the top five rows of the DataFrame and examine the similarity scores.

Exercice interactif pratique

Essayez cet exercice en complétant cet exemple de code.

# Import cosine_similarity measure
from sklearn.metrics.pairwise import ____

# Create the array of cosine similarity values
cosine_similarity_array = ____(tfidf_summary_df)

# Wrap the array in a pandas DataFrame
cosine_similarity_df = pd.____(cosine_similarity_array, ____=____.____, ____=____.____)

# Print the top 5 rows of the DataFrame
print(cosine_similarity_df.head())

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